Predicting residential demand: applying random forest to predict housing demand in Cape Town

Dyer, Ross

Predicting residential demand: applying random forest to predict housing demand in Cape Town

Master Thesis

2018

Publisher

University of Cape Town

Department

Department of Construction Economics and Management

Faculty

Faculty of Engineering and the Built Environment

Abstract

The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number of Bedrooms to varying degrees of accuracy. While none of the sets of models produced indicate a high degree of predictive accuracy relative to hurdle rates, the paper does demonstrate the value that the Random Forest technique offers in moving closer to an understanding of the complex nature of housing demand. A key finding is that the Census variables available for the models are not discriminatory enough to enable the high degree of accuracy expected from a predictive model.

Keywords

Property Studies

Reference:

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